The report was generated using the JSEQ_scRNAseq single-cell analysis pipeline. For more information, please visit: https://github.com/jkubis96/JSEQ_scRNAseq
Analysis Configuration Parameters
|
Parameter
|
Value
|
|
mt_per
|
25
|
|
scale_factor
|
1e+06
|
|
n_features
|
2000
|
|
c_res
|
0.5
|
|
heterogeneity
|
deg
|
|
mt_cssg
|
FALSE
|
|
m_val
|
0.05
|
|
top_m
|
50
|
|
max_genes
|
1000
|
|
max_combine
|
1000
|
|
loss_val
|
0.05
|
|
s_factor
|
0.8
|
|
p_bin
|
0.05
|
|
drop
|
TRUE
|
|
min_c
|
10
|
The parameters above were used during the current analysis.
If you need to apply different analysis conditions, modify the parameters in the configuration file.
Parameters description:
- mt_per
Maximum percentage of mitochondrial genes per cell.
Default: 25%
- down
Lower threshold for the number of genes per cell.
Default: NA (automatically computed if NA)
- up
Upper threshold for the number of genes per cell.
Default: NA (automatically computed if NA)
- scale_factor
Scale factor used for data normalization.
- n_features
Number of variable features to detect for clustering.
- c_res
Clustering resolution — higher values produce more clusters.
- heterogeneity
Method for estimating cluster heterogeneity.
Options: var (within-cluster variance), deg (deregulated gene profiles).
Default: deg
- mt_cssg
Whether to include mitochondrial genes when creating subclasses and subtypes.
Options: TRUE, FALSE.
Default: FALSE
- m_val
Maximum p-value threshold for marker detection in advanced subtype analyses.
Default: 0.05
- top_m
Maximum number of top markers used for naming clusters based on effect size metrics.
Default: 50
- max_genes
Maximum number of input genes considered in cluster heterogeneity discovery.
Default: 1000
- max_combine
Maximum number of initial combinations for each iteration during heterogeneity discovery.
Default: 1000
- loss_val
Assigned value for potentially unclassified cells within a cluster.
- s_factor
Maximum split factor for gene occurrence in heterogeneity discovery (0.2–1).
Default: 0.8
- p_bin
Minimum cell proportion required for population presence based on binomial test p-value.
Default: 0.05
- min_c
Minimum cell proportion required for population presence as defined by the user (independent of binomial test).
Default: 10
- drop
Boolean indicating whether to drop non-significant subtypes based on p_bin and min_c.
Default: TRUE
The configuration file is available in the directory:
JSEQ_scRNAseq/requirements_file/config_file.conf
Additional configuration files for other analysis steps can also be found in:
JSEQ_scRNAseq/requirements_file
For more information, please visit:
https://github.com/jkubis96/JSEQ_scRNAseq
Cell content analysis
Ratio of number of genes to counts

Number of genes and counts per cell

Percentage of ribosomal and mitochondrial genes [%]

Quality control of cell content
Genes per cell content & thresholds

Genes upper & lower thresholds per cell

Number of cells across different stages of analysis

Gene expression analysis across cells
Top highly variable genes in the dataset

Princilpe component selection
ElbowPlot - principal components cutoff

JackStrawPlot - PC significance
